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A Beta Mixture Model for Careless Respondent Detection in Visual Analogue Scale Data.

Lijin Zhang1, Benjamin W Domingue1, Leonie V D E Vogelsmeier2

  • 1Graduate School of Education, Stanford Universityhttps://ror.org/00f54p054, Stanford, CA, USA.

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Summary
This summary is machine-generated.

This study introduces a new model to detect careless responding in Visual Analogue Scales (VASs). The model improves data quality by identifying and accounting for respondents who are not fully engaged.

Keywords:
careless respondentsmixture modelingvisual analogue scale (VAS)

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Area of Science:

  • Psychometrics
  • Psychological and Medical Research

Background:

  • Visual Analogue Scales (VASs) are widely used but may increase careless responding.
  • Existing methods for detecting careless responses are not suitable for VAS data.

Purpose of the Study:

  • To develop and evaluate a model-based approach for detecting careless respondents in VAS data.
  • To integrate VAS measurement models with mixture item response theory for careless responding.

Main Methods:

  • Developed a novel model combining VAS measurement models with mixture item response theory.
  • Evaluated the model's effectiveness using simulation studies and real-world data from VAS and Likert scales.

Main Results:

  • The proposed model effectively detects careless responding and recovers key parameters.
  • VAS data showed a higher proportion of careless respondents compared to Likert scale data.
  • Item parameters estimated by the new model demonstrated improved psychometric properties.

Conclusions:

  • The new model enhances data quality in research utilizing Visual Analogue Scales.
  • This approach provides a robust method for identifying and addressing careless responding in VAS and Likert scale data.